The recognition of pipeline features contributes to its safe management by preventing severe consequences such as leakage resulting from bending deformation and denting under external pressure. However, extracting features of such a facility is complex and challenging when machine learning techniques are applied to feature recognition. Hence, this paper proposes a feature recognition technique for gas pipelines based on Gramian Time Frequency Enhancement Net (GTFE-Net), Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism (AM), namely GTFE-Net-BiLSTM-AM. Specifically, GTFE-Net is applied to enhance the time-frequency input bending strain signal, which is subsequently incorporated with the BiLSTM model to extract spatio-temporal features. The attention mechanism computes the corresponding weight of output features. The results show that the proposed method's recognition accuracy reaches 93.7%. The comparison study with the existing models validates the proposed method's superiority and shows that its accuracy is higher than that of the existing models (more than 0.9%) or their combined models (more than 1.1%). Overall, the proposed method contributes to the safety, reliability, and operation of natural gas pipelines.

中文翻译：

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GTFE-Net-BiLSTM-AM：一种天然气管道智能特征识别方法

对管道特征的识别有助于管道的安全管理，防止因弯曲变形和外压凹陷而导致泄漏等严重后果。然而，当机器学习技术应用于特征识别时，提取此类设施的特征是复杂且具有挑战性的。因此，本文提出一种基于格拉米亚时频增强网络（GTFE-Net）、双向长短期记忆（BiLSTM）和注意力机制（AM）的天然气管道特征识别技术，即GTFE-Net-BiLSTM-是。具体来说，GTFE-Net用于增强时频输入弯曲应变信号，随后与BiLSTM模型结合以提取时空特征。注意力机制计算输出特征的相应权重。结果表明，该方法的识别准确率达到93.7%。与现有模型的对比研究验证了该方法的优越性，表明其准确率高于现有模型（大于0.9%）或其组合模型（大于1.1%）。总体而言，所提出的方法有助于天然气管道的安全性、可靠性和运行。